Overview

Dataset statistics

Number of variables15
Number of observations210
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 KiB
Average record size in memory120.6 B

Variable types

Numeric12
Categorical3

Alerts

country has a high cardinality: 210 distinct values High cardinality
country code has a high cardinality: 210 distinct values High cardinality
countryiso3code has a high cardinality: 210 distinct values High cardinality
NE.EXP.GNFS.ZS is highly correlated with NE.IMP.GNFS.ZSHigh correlation
BX.KLT.DINV.CD.WD is highly correlated with NY.GDP.MKTP.CD and 1 other fieldsHigh correlation
NY.GDP.MKTP.CD is highly correlated with BX.KLT.DINV.CD.WD and 2 other fieldsHigh correlation
NE.IMP.GNFS.ZS is highly correlated with NE.EXP.GNFS.ZSHigh correlation
EN.POP.DNST is highly correlated with AG.SRF.TOTL.K2High correlation
SP.POP.GROW is highly correlated with SP.URB.GROWHigh correlation
SP.POP.TOTL is highly correlated with BX.KLT.DINV.CD.WD and 2 other fieldsHigh correlation
AG.SRF.TOTL.K2 is highly correlated with NY.GDP.MKTP.CD and 2 other fieldsHigh correlation
SP.URB.GROW is highly correlated with SP.POP.GROWHigh correlation
NE.EXP.GNFS.ZS is highly correlated with NE.IMP.GNFS.ZSHigh correlation
BX.KLT.DINV.CD.WD is highly correlated with NY.GDP.MKTP.CD and 2 other fieldsHigh correlation
NY.GDP.MKTP.CD is highly correlated with BX.KLT.DINV.CD.WD and 1 other fieldsHigh correlation
NE.IMP.GNFS.ZS is highly correlated with NE.EXP.GNFS.ZSHigh correlation
SP.POP.GROW is highly correlated with SP.URB.GROWHigh correlation
SP.POP.TOTL is highly correlated with BX.KLT.DINV.CD.WDHigh correlation
AG.SRF.TOTL.K2 is highly correlated with BX.KLT.DINV.CD.WD and 1 other fieldsHigh correlation
SP.URB.GROW is highly correlated with SP.POP.GROWHigh correlation
NE.EXP.GNFS.ZS is highly correlated with NE.IMP.GNFS.ZSHigh correlation
BX.KLT.DINV.CD.WD is highly correlated with NY.GDP.MKTP.CDHigh correlation
NY.GDP.MKTP.CD is highly correlated with BX.KLT.DINV.CD.WD and 1 other fieldsHigh correlation
NE.IMP.GNFS.ZS is highly correlated with NE.EXP.GNFS.ZSHigh correlation
SP.POP.GROW is highly correlated with SP.URB.GROWHigh correlation
SP.POP.TOTL is highly correlated with NY.GDP.MKTP.CD and 1 other fieldsHigh correlation
AG.SRF.TOTL.K2 is highly correlated with SP.POP.TOTLHigh correlation
SP.URB.GROW is highly correlated with SP.POP.GROWHigh correlation
NE.EXP.GNFS.ZS is highly correlated with NE.IMP.GNFS.ZS and 1 other fieldsHigh correlation
BX.KLT.DINV.CD.WD is highly correlated with NY.GDP.MKTP.CD and 2 other fieldsHigh correlation
NY.GDP.MKTP.CD is highly correlated with BX.KLT.DINV.CD.WD and 2 other fieldsHigh correlation
NY.GDP.MKTP.KD.ZG is highly correlated with EN.POP.DNST and 2 other fieldsHigh correlation
NE.IMP.GNFS.ZS is highly correlated with NE.EXP.GNFS.ZS and 1 other fieldsHigh correlation
EN.POP.DNST is highly correlated with NE.EXP.GNFS.ZS and 2 other fieldsHigh correlation
SP.POP.GROW is highly correlated with NY.GDP.MKTP.KD.ZG and 1 other fieldsHigh correlation
SP.POP.TOTL is highly correlated with BX.KLT.DINV.CD.WD and 2 other fieldsHigh correlation
AG.SRF.TOTL.K2 is highly correlated with BX.KLT.DINV.CD.WD and 2 other fieldsHigh correlation
SP.URB.GROW is highly correlated with NY.GDP.MKTP.KD.ZG and 1 other fieldsHigh correlation
df_index is uniformly distributed Uniform
country is uniformly distributed Uniform
country code is uniformly distributed Uniform
countryiso3code is uniformly distributed Uniform
df_index has unique values Unique
country has unique values Unique
country code has unique values Unique
countryiso3code has unique values Unique
EN.POP.DNST has unique values Unique
SP.POP.GROW has unique values Unique
SP.POP.TOTL has unique values Unique
NE.EXP.GNFS.ZS has 30 (14.3%) zeros Zeros
BX.KLT.DINV.CD.WD has 20 (9.5%) zeros Zeros
NY.GDP.MKTP.CD has 7 (3.3%) zeros Zeros
NY.GDP.MKTP.KD.ZG has 10 (4.8%) zeros Zeros
NE.IMP.GNFS.ZS has 30 (14.3%) zeros Zeros
NY.GDP.DEFL.KD.ZG has 10 (4.8%) zeros Zeros

Reproduction

Analysis started2022-07-20 13:57:21.969211
Analysis finished2022-07-20 13:57:42.459050
Duration20.49 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2204.5
Minimum10
Maximum4399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:42.535522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile229.45
Q11107.25
median2204.5
Q33301.75
95-th percentile4179.55
Maximum4399
Range4389
Interquartile range (IQR)2194.5

Descriptive statistics

Standard deviation1276.084833
Coefficient of variation (CV)0.5788545396
Kurtosis-1.2
Mean2204.5
Median Absolute Deviation (MAD)1102.5
Skewness0
Sum462945
Variance1628392.5
MonotonicityStrictly increasing
2022-07-20T19:27:42.676621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101
 
0.5%
33071
 
0.5%
28031
 
0.5%
28241
 
0.5%
28451
 
0.5%
28661
 
0.5%
28871
 
0.5%
29081
 
0.5%
29291
 
0.5%
29501
 
0.5%
Other values (200)200
95.2%
ValueCountFrequency (%)
101
0.5%
311
0.5%
521
0.5%
731
0.5%
941
0.5%
1151
0.5%
1361
0.5%
1571
0.5%
1781
0.5%
1991
0.5%
ValueCountFrequency (%)
43991
0.5%
43781
0.5%
43571
0.5%
43361
0.5%
43151
0.5%
42941
0.5%
42731
0.5%
42521
0.5%
42311
0.5%
42101
0.5%

country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Afghanistan
 
1
Rwanda
 
1
Nepal
 
1
Netherlands
 
1
New Caledonia
 
1
Other values (205)
205 

Length

Max length30
Median length22
Mean length9.557142857
Min length4

Characters and Unicode

Total characters2007
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.5%
Rwanda1
 
0.5%
Nepal1
 
0.5%
Netherlands1
 
0.5%
New Caledonia1
 
0.5%
New Zealand1
 
0.5%
Nicaragua1
 
0.5%
Niger1
 
0.5%
Nigeria1
 
0.5%
North Macedonia1
 
0.5%
Other values (200)200
95.2%

Length

2022-07-20T19:27:42.809036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands8
 
2.7%
and7
 
2.3%
rep7
 
2.3%
republic6
 
2.0%
st4
 
1.3%
the3
 
1.0%
new3
 
1.0%
united3
 
1.0%
arab3
 
1.0%
guinea3
 
1.0%
Other values (244)253
84.3%

Most occurring characters

ValueCountFrequency (%)
a284
 
14.2%
i159
 
7.9%
n153
 
7.6%
e140
 
7.0%
r112
 
5.6%
o101
 
5.0%
90
 
4.5%
u73
 
3.6%
t72
 
3.6%
s71
 
3.5%
Other values (47)752
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1584
78.9%
Uppercase Letter295
 
14.7%
Space Separator90
 
4.5%
Other Punctuation30
 
1.5%
Close Punctuation3
 
0.1%
Open Punctuation3
 
0.1%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a284
17.9%
i159
10.0%
n153
9.7%
e140
8.8%
r112
 
7.1%
o101
 
6.4%
u73
 
4.6%
t72
 
4.5%
s71
 
4.5%
l70
 
4.4%
Other values (16)349
22.0%
Uppercase Letter
ValueCountFrequency (%)
S33
 
11.2%
M24
 
8.1%
C22
 
7.5%
B22
 
7.5%
R20
 
6.8%
A19
 
6.4%
I18
 
6.1%
G16
 
5.4%
T15
 
5.1%
P14
 
4.7%
Other values (14)92
31.2%
Other Punctuation
ValueCountFrequency (%)
.17
56.7%
,11
36.7%
'2
 
6.7%
Space Separator
ValueCountFrequency (%)
90
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1879
93.6%
Common128
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a284
15.1%
i159
 
8.5%
n153
 
8.1%
e140
 
7.5%
r112
 
6.0%
o101
 
5.4%
u73
 
3.9%
t72
 
3.8%
s71
 
3.8%
l70
 
3.7%
Other values (40)644
34.3%
Common
ValueCountFrequency (%)
90
70.3%
.17
 
13.3%
,11
 
8.6%
)3
 
2.3%
(3
 
2.3%
'2
 
1.6%
-2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a284
 
14.2%
i159
 
7.9%
n153
 
7.6%
e140
 
7.0%
r112
 
5.6%
o101
 
5.0%
90
 
4.5%
u73
 
3.6%
t72
 
3.6%
s71
 
3.5%
Other values (47)752
37.5%

country code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
AF
 
1
RW
 
1
NP
 
1
NL
 
1
NC
 
1
Other values (205)
205 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters420
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)100.0%

Sample

1st rowAF
2nd rowAL
3rd rowDZ
4th rowAS
5th rowAD

Common Values

ValueCountFrequency (%)
AF1
 
0.5%
RW1
 
0.5%
NP1
 
0.5%
NL1
 
0.5%
NC1
 
0.5%
NZ1
 
0.5%
NI1
 
0.5%
NE1
 
0.5%
NG1
 
0.5%
MK1
 
0.5%
Other values (200)200
95.2%

Length

2022-07-20T19:27:42.918008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
af1
 
0.5%
cz1
 
0.5%
bw1
 
0.5%
az1
 
0.5%
dz1
 
0.5%
as1
 
0.5%
ad1
 
0.5%
ao1
 
0.5%
ag1
 
0.5%
ar1
 
0.5%
Other values (200)200
95.2%

Most occurring characters

ValueCountFrequency (%)
M34
 
8.1%
S28
 
6.7%
G26
 
6.2%
A23
 
5.5%
B23
 
5.5%
C23
 
5.5%
T23
 
5.5%
N20
 
4.8%
L19
 
4.5%
E19
 
4.5%
Other values (16)182
43.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter420
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M34
 
8.1%
S28
 
6.7%
G26
 
6.2%
A23
 
5.5%
B23
 
5.5%
C23
 
5.5%
T23
 
5.5%
N20
 
4.8%
L19
 
4.5%
E19
 
4.5%
Other values (16)182
43.3%

Most occurring scripts

ValueCountFrequency (%)
Latin420
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M34
 
8.1%
S28
 
6.7%
G26
 
6.2%
A23
 
5.5%
B23
 
5.5%
C23
 
5.5%
T23
 
5.5%
N20
 
4.8%
L19
 
4.5%
E19
 
4.5%
Other values (16)182
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M34
 
8.1%
S28
 
6.7%
G26
 
6.2%
A23
 
5.5%
B23
 
5.5%
C23
 
5.5%
T23
 
5.5%
N20
 
4.8%
L19
 
4.5%
E19
 
4.5%
Other values (16)182
43.3%

countryiso3code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
AFG
 
1
RWA
 
1
NPL
 
1
NLD
 
1
NCL
 
1
Other values (205)
205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters630
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND

Common Values

ValueCountFrequency (%)
AFG1
 
0.5%
RWA1
 
0.5%
NPL1
 
0.5%
NLD1
 
0.5%
NCL1
 
0.5%
NZL1
 
0.5%
NIC1
 
0.5%
NER1
 
0.5%
NGA1
 
0.5%
MKD1
 
0.5%
Other values (200)200
95.2%

Length

2022-07-20T19:27:43.019984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg1
 
0.5%
cze1
 
0.5%
bwa1
 
0.5%
aze1
 
0.5%
dza1
 
0.5%
asm1
 
0.5%
and1
 
0.5%
ago1
 
0.5%
atg1
 
0.5%
arg1
 
0.5%
Other values (200)200
95.2%

Most occurring characters

ValueCountFrequency (%)
A47
 
7.5%
R47
 
7.5%
M46
 
7.3%
N44
 
7.0%
L35
 
5.6%
S35
 
5.6%
B34
 
5.4%
G31
 
4.9%
T30
 
4.8%
C28
 
4.4%
Other values (16)253
40.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter630
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A47
 
7.5%
R47
 
7.5%
M46
 
7.3%
N44
 
7.0%
L35
 
5.6%
S35
 
5.6%
B34
 
5.4%
G31
 
4.9%
T30
 
4.8%
C28
 
4.4%
Other values (16)253
40.2%

Most occurring scripts

ValueCountFrequency (%)
Latin630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A47
 
7.5%
R47
 
7.5%
M46
 
7.3%
N44
 
7.0%
L35
 
5.6%
S35
 
5.6%
B34
 
5.4%
G31
 
4.9%
T30
 
4.8%
C28
 
4.4%
Other values (16)253
40.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A47
 
7.5%
R47
 
7.5%
M46
 
7.3%
N44
 
7.0%
L35
 
5.6%
S35
 
5.6%
B34
 
5.4%
G31
 
4.9%
T30
 
4.8%
C28
 
4.4%
Other values (16)253
40.2%

NE.EXP.GNFS.ZS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct181
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.05432139
Minimum0
Maximum299.3755782
Zeros30
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:43.134608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.86981574
median30.8382353
Q348.60927562
95-th percentile78.36301702
Maximum299.3755782
Range299.3755782
Interquartile range (IQR)31.73945988

Descriptive statistics

Standard deviation33.27727967
Coefficient of variation (CV)0.9229761755
Kurtosis21.31942025
Mean36.05432139
Median Absolute Deviation (MAD)15.92594385
Skewness3.413033696
Sum7571.407492
Variance1107.377342
MonotonicityNot monotonic
2022-07-20T19:27:43.262647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030
 
14.3%
28.443954511
 
0.5%
48.281954481
 
0.5%
51.851851851
 
0.5%
9.5825361721
 
0.5%
69.803672481
 
0.5%
20.199474921
 
0.5%
30.273627681
 
0.5%
40.466605721
 
0.5%
16.18441461
 
0.5%
Other values (171)171
81.4%
ValueCountFrequency (%)
030
14.3%
6.3783765981
 
0.5%
8.5654251321
 
0.5%
8.5702881581
 
0.5%
9.5825361721
 
0.5%
9.6389046351
 
0.5%
9.7000016391
 
0.5%
10.623673251
 
0.5%
10.748439691
 
0.5%
10.865584771
 
0.5%
ValueCountFrequency (%)
299.37557821
0.5%
197.99896081
0.5%
162.70985711
0.5%
150.94674241
0.5%
103.24656631
0.5%
93.798780861
0.5%
86.930295071
0.5%
85.75843321
0.5%
82.242385641
0.5%
81.073087591
0.5%

BX.KLT.DINV.CD.WD
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct191
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8679640265
Minimum-2.200732516 × 1010
Maximum2.64039 × 1011
Zeros20
Zeros (%)9.5%
Negative8
Negative (%)3.8%
Memory size1.8 KiB
2022-07-20T19:27:43.400795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.200732516 × 1010
5-th percentile0
Q194567971.77
median531124186.1
Q32740225000
95-th percentile3.902093846 × 1010
Maximum2.64039 × 1011
Range2.860463252 × 1011
Interquartile range (IQR)2645657028

Descriptive statistics

Standard deviation2.97542209 × 1010
Coefficient of variation (CV)3.428047706
Kurtosis45.40789292
Mean8679640265
Median Absolute Deviation (MAD)531124186.1
Skewness6.18538391
Sum1.822724456 × 1012
Variance8.853136612 × 1020
MonotonicityNot monotonic
2022-07-20T19:27:43.527451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020
 
9.5%
1907744321
 
0.5%
12426527961
 
0.5%
87741711.631
 
0.5%
1.157532732 × 10111
 
0.5%
14390855731
 
0.5%
2863069031
 
0.5%
4899000001
 
0.5%
796636157.91
 
0.5%
60262530911
 
0.5%
Other values (181)181
86.2%
ValueCountFrequency (%)
-2.200732516 × 10101
 
0.5%
-2.077028026 × 10101
 
0.5%
-1.176786193 × 10101
 
0.5%
-32272111821
 
0.5%
-2557000001
 
0.5%
-113160683.71
 
0.5%
-9356673.1321
 
0.5%
-6602470.8681
 
0.5%
020
9.5%
336435.06881
 
0.5%
ValueCountFrequency (%)
2.64039 × 10111
0.5%
2.437034346 × 10111
0.5%
1.254918949 × 10111
0.5%
1.157532732 × 10111
0.5%
8.60375021 × 10101
0.5%
8.238993247 × 10101
0.5%
6.673455129 × 10101
0.5%
5.532243416 × 10101
0.5%
4.316778 × 10101
0.5%
4.238682491 × 10101
0.5%

NY.GDP.MKTP.CD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct204
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.087405948 × 1011
Minimum0
Maximum1.504896444 × 1013
Zeros7
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:43.664474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile171898255
Q14198743112
median1.674561162 × 1010
Q31.369453255 × 1011
95-th percentile1.478653298 × 1012
Maximum1.504896444 × 1013
Range1.504896444 × 1013
Interquartile range (IQR)1.327465824 × 1011

Descriptive statistics

Standard deviation1.255886832 × 1012
Coefficient of variation (CV)4.067773573
Kurtosis94.70820689
Mean3.087405948 × 1011
Median Absolute Deviation (MAD)1.604199378 × 1010
Skewness8.850354373
Sum6.483552491 × 1013
Variance1.577251736 × 1024
MonotonicityNot monotonic
2022-07-20T19:27:43.797814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
3.3%
6.499349805 × 10101
 
0.5%
1.600265643 × 10101
 
0.5%
8.47380859 × 10111
 
0.5%
93643473721
 
0.5%
1.465175412 × 10111
 
0.5%
87586390961
 
0.5%
78511918991
 
0.5%
3.614566222 × 10111
 
0.5%
94071687021
 
0.5%
Other values (194)194
92.4%
ValueCountFrequency (%)
07
3.3%
32104201.061
 
0.5%
47564520.391
 
0.5%
155299728.51
 
0.5%
1604071001
 
0.5%
1859430001
 
0.5%
196652514.31
 
0.5%
2969441001
 
0.5%
366840012.41
 
0.5%
493825925.91
 
0.5%
ValueCountFrequency (%)
1.504896444 × 10131
0.5%
6.087163875 × 10121
0.5%
5.759071769 × 10121
0.5%
3.39966782 × 10121
0.5%
2.645187882 × 10121
0.5%
2.491110093 × 10121
0.5%
2.208838109 × 10121
0.5%
2.136099955 × 10121
0.5%
1.675615336 × 10121
0.5%
1.617343367 × 10121
0.5%

NY.GDP.MKTP.KD.ZG
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9106875
Minimum-8.924175889
Maximum19.67532314
Zeros10
Zeros (%)4.8%
Negative23
Negative (%)11.0%
Memory size1.8 KiB
2022-07-20T19:27:43.945190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-8.924175889
5-th percentile-2.619895483
Q11.53923984
median3.750467437
Q36.393214055
95-th percentile10.22182716
Maximum19.67532314
Range28.59949903
Interquartile range (IQR)4.853974215

Descriptive statistics

Standard deviation4.194407376
Coefficient of variation (CV)1.072549872
Kurtosis1.660546065
Mean3.9106875
Median Absolute Deviation (MAD)2.440557422
Skewness0.2973997712
Sum821.244375
Variance17.59305324
MonotonicityNot monotonic
2022-07-20T19:27:44.076711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
4.8%
1.6066886291
 
0.5%
4.8164146511
 
0.5%
1.3427393361
 
0.5%
1.5238775711
 
0.5%
4.4096454991
 
0.5%
8.5781667431
 
0.5%
8.0056559151
 
0.5%
3.3587508581
 
0.5%
1.3959390861
 
0.5%
Other values (191)191
91.0%
ValueCountFrequency (%)
-8.9241758891
0.5%
-7.8405947441
0.5%
-5.6515115341
0.5%
-5.4786274321
0.5%
-5.4736842111
0.5%
-4.4709418851
0.5%
-4.470469051
0.5%
-3.901236281
0.5%
-2.8327749151
0.5%
-2.7325959661
0.5%
ValueCountFrequency (%)
19.675323141
0.5%
19.592331531
0.5%
14.519749711
0.5%
14.362441471
0.5%
13.550100861
0.5%
12.550538351
0.5%
11.9458961
0.5%
11.111111111
0.5%
11.095231271
0.5%
10.635871061
0.5%

NE.IMP.GNFS.ZS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct181
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.28895891
Minimum0
Maximum287.0259019
Zeros30
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:44.377270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124.48924201
median36.65104892
Q355.48909748
95-th percentile84.58165128
Maximum287.0259019
Range287.0259019
Interquartile range (IQR)30.99985547

Descriptive statistics

Standard deviation32.78722963
Coefficient of variation (CV)0.7940919436
Kurtosis15.68693721
Mean41.28895891
Median Absolute Deviation (MAD)15.52700868
Skewness2.650542403
Sum8670.681372
Variance1075.002427
MonotonicityNot monotonic
2022-07-20T19:27:44.507384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030
 
14.3%
42.196745791
 
0.5%
59.79052271
 
0.5%
48.148148151
 
0.5%
36.40236961
 
0.5%
61.718401661
 
0.5%
47.969007481
 
0.5%
27.977987821
 
0.5%
59.8963931
 
0.5%
35.76157171
 
0.5%
Other values (171)171
81.4%
ValueCountFrequency (%)
030
14.3%
8.1772126451
 
0.5%
11.906593341
 
0.5%
13.579969241
 
0.5%
15.878534431
 
0.5%
16.037189861
 
0.5%
17.606486541
 
0.5%
17.660145551
 
0.5%
17.721676411
 
0.5%
17.923758411
 
0.5%
ValueCountFrequency (%)
287.02590191
0.5%
171.6865951
0.5%
150.88995521
0.5%
130.96201981
0.5%
125.84751811
0.5%
110.85338491
0.5%
108.08182321
0.5%
107.97725251
0.5%
94.415357771
0.5%
88.39267861
0.5%

NY.GDP.DEFL.KD.ZG
Real number (ℝ)

ZEROS

Distinct201
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.463473949
Minimum-19
Maximum48.58168995
Zeros10
Zeros (%)4.8%
Negative17
Negative (%)8.1%
Memory size1.8 KiB
2022-07-20T19:27:44.644139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile-0.9623512521
Q11.059147942
median3.988770888
Q39.127428804
95-th percentile22.74083199
Maximum48.58168995
Range67.58168995
Interquartile range (IQR)8.068280862

Descriptive statistics

Standard deviation9.057668844
Coefficient of variation (CV)1.401362319
Kurtosis5.544223978
Mean6.463473949
Median Absolute Deviation (MAD)3.37953808
Skewness1.775190539
Sum1357.329529
Variance82.04136488
MonotonicityNot monotonic
2022-07-20T19:27:44.772901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
4.8%
10.850239461
 
0.5%
15.146932441
 
0.5%
0.94041373181
 
0.5%
3.0790595461
 
0.5%
6.1389459831
 
0.5%
3.4750430141
 
0.5%
16.342766331
 
0.5%
2.0412830231
 
0.5%
-0.88050314471
 
0.5%
Other values (191)191
91.0%
ValueCountFrequency (%)
-191
0.5%
-16.22819971
0.5%
-15.364889551
0.5%
-4.3057727461
0.5%
-4.2127237661
0.5%
-2.9750735931
0.5%
-1.880741951
0.5%
-1.4253139921
0.5%
-1.216642071
0.5%
-1.0562685221
0.5%
ValueCountFrequency (%)
48.581689951
0.5%
45.94326871
0.5%
42.30103391
0.5%
39.178187961
0.5%
33.304932151
0.5%
32.053010251
0.5%
31.688176251
0.5%
29.41768381
0.5%
24.906534181
0.5%
23.616292021
0.5%

EN.POP.DNST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.9692369
Minimum0
Maximum17567.3409
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:44.908292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.799252629
Q133.02788036
median81.28145065
Q3188.4884654
95-th percentile592.9408251
Maximum17567.3409
Range17567.3409
Interquartile range (IQR)155.460585

Descriptive statistics

Standard deviation1315.356673
Coefficient of variation (CV)4.783650299
Kurtosis147.9055909
Mean274.9692369
Median Absolute Deviation (MAD)62.25516763
Skewness11.70077742
Sum57743.53976
Variance1730163.176
MonotonicityNot monotonic
2022-07-20T19:27:45.039669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.704088171
 
0.5%
406.94519661
 
0.5%
188.4423231
 
0.5%
492.59988141
 
0.5%
13.662472651
 
0.5%
16.523109641
 
0.5%
48.39669271
 
0.5%
12.997572431
 
0.5%
174.0320861
 
0.5%
81.483108641
 
0.5%
Other values (200)200
95.2%
ValueCountFrequency (%)
01
0.5%
0.13864051651
0.5%
1.7507543961
0.5%
2.5736702741
0.5%
2.8678585841
0.5%
3.1724788031
0.5%
3.3901232171
0.5%
3.3918333331
0.5%
3.506265771
0.5%
3.5223223121
0.5%
ValueCountFrequency (%)
17567.34091
0.5%
7231.8119661
0.5%
1628.4304461
0.5%
1295.33751
0.5%
1219.11
0.5%
12061
0.5%
1133.7130911
0.5%
1001.6470591
0.5%
690.84558821
0.5%
656.11860471
0.5%

SP.POP.GROW
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.397956237
Minimum-2.09694342
Maximum11.48337102
Zeros0
Zeros (%)0.0%
Negative30
Negative (%)14.3%
Memory size1.8 KiB
2022-07-20T19:27:45.180233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.09694342
5-th percentile-0.5018014149
Q10.4381089021
median1.191326068
Q32.324205409
95-th percentile3.531206196
Maximum11.48337102
Range13.58031444
Interquartile range (IQR)1.886096507

Descriptive statistics

Standard deviation1.557328423
Coefficient of variation (CV)1.114003702
Kurtosis8.800019899
Mean1.397956237
Median Absolute Deviation (MAD)0.9346412319
Skewness1.81486404
Sum293.5708097
Variance2.425271817
MonotonicityNot monotonic
2022-07-20T19:27:45.323894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7466146381
 
0.5%
2.5888500811
 
0.5%
0.48120259241
 
0.5%
0.51292310061
 
0.5%
1.5332154291
 
0.5%
1.1117260561
 
0.5%
1.3573716071
 
0.5%
3.8441666561
 
0.5%
2.671442831
 
0.5%
0.21107377511
 
0.5%
Other values (200)200
95.2%
ValueCountFrequency (%)
-2.096943421
0.5%
-2.081305091
0.5%
-1.8541531071
0.5%
-1.0482629531
0.5%
-0.8188533361
0.5%
-0.78311810441
0.5%
-0.72947525691
0.5%
-0.65827544661
0.5%
-0.59395918361
0.5%
-0.53299098271
0.5%
ValueCountFrequency (%)
11.483371021
0.5%
7.6872221671
0.5%
5.8797323541
0.5%
5.5864332591
0.5%
5.2048232791
0.5%
4.6001847451
0.5%
4.5585975361
0.5%
3.9265272771
0.5%
3.8441666561
0.5%
3.6714933331
0.5%

SP.POP.TOTL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32415351.14
Minimum10009
Maximum1337705000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:45.468074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10009
5-th percentile48346.6
Q1772121
median6003967.5
Q320996966
95-th percentile105036181.3
Maximum1337705000
Range1337694991
Interquartile range (IQR)20224845

Descriptive statistics

Standard deviation129824256.9
Coefficient of variation (CV)4.005023928
Kurtosis83.61330917
Mean32415351.14
Median Absolute Deviation (MAD)5826735.5
Skewness8.834548855
Sum6807223739
Variance1.685433769 × 1016
MonotonicityNot monotonic
2022-07-20T19:27:45.614687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291855111
 
0.5%
100393381
 
0.5%
270132071
 
0.5%
166153941
 
0.5%
2497501
 
0.5%
43507001
 
0.5%
58240581
 
0.5%
164640251
 
0.5%
1585032031
 
0.5%
20550041
 
0.5%
Other values (200)200
95.2%
ValueCountFrequency (%)
100091
0.5%
105211
0.5%
179541
0.5%
277961
0.5%
312211
0.5%
326581
0.5%
340561
0.5%
356091
0.5%
359961
0.5%
375821
0.5%
ValueCountFrequency (%)
13377050001
0.5%
12342811631
0.5%
3093271431
0.5%
2418342261
0.5%
1957136371
0.5%
1794246431
0.5%
1585032031
0.5%
1475754331
0.5%
1428494681
0.5%
1280700001
0.5%

AG.SRF.TOTL.K2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct205
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623495.6717
Minimum0
Maximum17098250
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-07-20T19:27:45.761623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile169
Q111067.5
median99338.95
Q3447187.5
95-th percentile2257033.5
Maximum17098250
Range17098250
Interquartile range (IQR)436120

Descriptive statistics

Standard deviation1852522.417
Coefficient of variation (CV)2.971187293
Kurtosis39.29973349
Mean623495.6717
Median Absolute Deviation (MAD)98903.95
Skewness5.818019375
Sum130934091.1
Variance3.431839305 × 1012
MonotonicityNot monotonic
2022-07-20T19:27:45.899147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4603
 
1.4%
7502
 
1.0%
1802
 
1.0%
02
 
1.0%
3851781
 
0.5%
1471801
 
0.5%
415401
 
0.5%
185801
 
0.5%
2677101
 
0.5%
1303701
 
0.5%
Other values (195)195
92.9%
ValueCountFrequency (%)
02
1.0%
201
0.5%
301
0.5%
341
0.5%
541
0.5%
54.41
0.5%
601
0.5%
74.921
0.5%
1501
0.5%
1601
0.5%
ValueCountFrequency (%)
170982501
0.5%
98797501
0.5%
98315101
0.5%
9600000.71
0.5%
85157701
0.5%
77412201
0.5%
32872601
0.5%
27804001
0.5%
27249021
0.5%
23817401
0.5%

SP.URB.GROW
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct209
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.031065107
Minimum-2.972201179
Maximum11.65102632
Zeros2
Zeros (%)1.0%
Negative29
Negative (%)13.8%
Memory size1.8 KiB
2022-07-20T19:27:46.045372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.972201179
5-th percentile-0.5323038738
Q10.5970791633
median1.668815808
Q33.322193125
95-th percentile5.320428376
Maximum11.65102632
Range14.62322749
Interquartile range (IQR)2.725113961

Descriptive statistics

Standard deviation2.008396716
Coefficient of variation (CV)0.988839161
Kurtosis1.946692687
Mean2.031065107
Median Absolute Deviation (MAD)1.361755795
Skewness0.8450002233
Sum426.5236725
Variance4.033657371
MonotonicityNot monotonic
2022-07-20T19:27:46.176420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
1.0%
3.6309987351
 
0.5%
6.3034676991
 
0.5%
2.4932183631
 
0.5%
1.4885841681
 
0.5%
2.2478009231
 
0.5%
1.042101661
 
0.5%
1.7023122121
 
0.5%
3.8194919441
 
0.5%
4.744297231
 
0.5%
Other values (199)199
94.8%
ValueCountFrequency (%)
-2.9722011791
0.5%
-2.2241530891
0.5%
-2.0414898141
0.5%
-1.1656747531
0.5%
-0.9041116871
0.5%
-0.78311810441
0.5%
-0.71613765941
0.5%
-0.64967882011
0.5%
-0.626233161
0.5%
-0.56904203141
0.5%
ValueCountFrequency (%)
11.651026321
0.5%
8.0983627181
0.5%
7.0736227081
0.5%
6.9869013861
0.5%
6.5614382761
0.5%
6.3034676991
0.5%
5.8797323541
0.5%
5.8265213941
0.5%
5.7875314481
0.5%
5.4031433311
0.5%

Interactions

2022-07-20T19:27:40.498429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:24.804972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.161206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.696844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.038604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.629731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.945366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.269436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.815458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.166648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.660492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.068575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.603002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.027110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.265227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.801148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.143035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.731212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.048308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.394928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.917221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.270712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.769108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.173388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.714059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.127461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.377253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.901200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.300482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.838789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.156371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.505206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.029033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.377399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.907994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.287511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.818887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.227895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.485754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.010782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.414272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.946496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.263806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.619303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.144086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.484619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.020258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.397574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.937476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.336335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.604697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.126470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.534376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.063449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.373539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.734486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.261349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.596306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.139639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.517351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.216061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.435435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.909098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.246971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.646951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.167898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.483973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.016094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.370084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.703935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.252140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.636300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.320860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.537134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.020203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.357124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.759344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.272074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.591172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.126225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.478689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.812417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.365744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.752459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.432026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.642311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.136175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.469509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.875855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.381490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.705750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.237240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.596257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.930473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.481304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.868629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.542619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.747631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.248148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.584166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:29.992565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.495102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.822286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.349983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.710356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.044506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.599387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:39.984630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.652885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.848635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.361015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.693898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.105222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.604030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:32.933708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.465437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.822043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.151592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.713530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.097419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.770864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:25.953552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.472342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.807928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.225698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.716714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.048385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.582541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:35.937980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.267653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.838318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.216144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:41.889463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:26.058799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:27.587035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:28.923384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:30.517872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:31.834784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:33.161800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:34.699169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:36.053380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:37.551636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:38.955539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T19:27:40.348598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-20T19:27:46.305490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-20T19:27:46.502957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-20T19:27:46.702057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-20T19:27:47.059290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-20T19:27:42.082134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-20T19:27:42.351711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcountrycountry codecountryiso3codeNE.EXP.GNFS.ZSBX.KLT.DINV.CD.WDNY.GDP.MKTP.CDNY.GDP.MKTP.KD.ZGNE.IMP.GNFS.ZSNY.GDP.DEFL.KD.ZGEN.POP.DNSTSP.POP.GROWSP.POP.TOTLAG.SRF.TOTL.K2SP.URB.GROW
010AfghanistanAFAFG0.0000001.907744e+081.585668e+1014.3624410.0000003.81463044.7040882.74661529185511.0652860.03.630999
131AlbaniaALALB27.9794351.089898e+091.192692e+103.70693848.5639554.493143106.314635-0.4964622913021.028750.01.609373
252AlgeriaDZDZA38.4445472.300369e+091.612073e+113.60000031.42211416.11998115.1055331.80500535977451.02381740.02.867782
373American SamoaASASM63.5253050.000000e+005.730000e+080.29985094.415358-15.364890280.420000-1.04826356084.0200.0-1.165675
494AndorraADAND0.0000000.000000e+003.449926e+09-1.9749580.0000000.374314179.689362-0.00828884454.0470.0-0.113253
5115AngolaAOAGO61.543116-3.227211e+098.169956e+104.86000042.58051931.68817618.7344573.67149323356247.01246700.04.932272
6136Antigua and BarbudaAGATG79.5875539.667921e+071.148700e+09-7.84059573.2583371.473357200.0681821.47279488030.0440.0-0.716138
7157ArgentinaARARG18.9338231.133272e+104.236274e+1110.12539816.03719020.91512414.9043020.75221840788453.02780400.00.926286
8178ArmeniaAMARM19.7484155.293214e+089.260285e+092.20000044.8945617.768715101.064770-0.3739552877314.029740.0-0.531464
9199ArubaAWABW60.2231331.867598e+082.453631e+09-2.73259675.250455-1.216642564.8055560.209731101665.0180.0-0.626233

Last rows

df_indexcountrycountry codecountryiso3codeNE.EXP.GNFS.ZSBX.KLT.DINV.CD.WDNY.GDP.MKTP.CDNY.GDP.MKTP.KD.ZGNE.IMP.GNFS.ZSNY.GDP.DEFL.KD.ZGEN.POP.DNSTSP.POP.GROWSP.POP.TOTLAG.SRF.TOTL.K2SP.URB.GROW
2004210United StatesUSUSA12.3413612.640390e+111.504896e+132.70885715.8785341.20179233.8157800.829617309327143.09831510.01.035345
2014231UruguayUYURY26.3429952.191068e+094.028448e+107.80341025.3560434.90702519.1936520.2860963359273.0176220.00.503469
2024252UzbekistanUZUZB24.2754051.662748e+094.976568e+107.59716824.98046548.58169067.1424542.82285028562400.0447400.03.777233
2034273VanuatuVUVUT48.7183836.310940e+076.707132e+081.26068255.1049301.46989019.3778512.560702236216.012190.03.282617
2044294Venezuela, RBVEVEN28.5304071.583000e+093.931924e+11-1.48879117.60648745.94326932.2430041.44831728439942.0912050.01.477840
2054315VietnamVNVNM0.0000008.000000e+091.471989e+116.4232380.00000042.301034283.7025671.00012987967655.0330954.03.177057
2064336Virgin Islands (U.S.)VIVIR299.3755780.000000e+004.324000e+090.596383287.0259022.317669309.591429-0.043366108357.0350.00.141565
2074357Yemen, Rep.YEYEM29.9950781.886418e+083.090675e+107.70230734.39055923.61629243.8563822.79576623154854.0527970.04.644309
2084378ZambiaZMZMB37.0259331.729300e+092.026556e+1010.29822330.87498613.95091318.3026222.91465513605986.0752610.04.177851
2094399ZimbabweZWZWE29.6408951.225867e+081.204166e+1019.67532353.4832954.09840532.8233891.35396412697728.0390760.00.807209